19,997 machine learning datasets
19,997 dataset results
Dataset Generation
Dataset Generation
Dataset provided along with the repository of the paper:
Aegis AI Content Safety Dataset is an open-source content safety dataset (CC-BY-4.0), which adheres to Nvidia's content safety taxonomy, covering 13 critical risk categories (see Dataset Description).
We use Something-Something v2 dataset to obtain the generation prompts and ground truth masks from real action videos. We filter out a set of 295 prompts. The details for this filtering are in the "Peekaboo: Interactive Video Generation via Masked-Diffusion" paper. We then use an off-the-shelf OWL-ViT-large open-vocabulary object detector to obtain the bounding box (bbox) annotations of the object in the videos. This set represents bbox and prompt pairs of real-world videos, serving as a test bed for both the quality and control of methods for generating realistic videos with spatio-temporal control.
A large image source dataset including 23 devices, 6978 nat images, 2427 flat original images, total 23,361 images, 38G size, with serial numbers and naming conventions standardized. Mimicking the transmission process of social networks, a JPEG compressed dataset transmitted through Ding Talk and WeChat was synchronized and produced for the experiment test work.
Overview Short-MetaWorld is a dataset rendered from modified environment of Meta-World [1], which contains Multi-Task10(MT10) and Meta-Learning10(ML10) in total 20 tasks with 100 successful trajectories for each task. Each trajectory is padded to 20 steps.
The DocRED Information Extraction (DocRED-IE) dataset extends the DocRED dataset for the Document-level Closed Information Extraction (DocIE) task. DocRED-IE is a multi-task dataset and allows for 5 subtasks: (i) Document-level Relation Extraction, (ii) Mention Detection, (iii) Entity Typing, (iv) Entity Disambiguation, (v) Coreference Resolution, as well as combinations thereof such as Named Entity Recognition (NER) or Entity Linking. The DocRED-IE dataset also allows for the end-to-end tasks of: (i) DocIE and (ii) Joint Entity and Relation Extraction. DocRED-IE comprises sentence-level and document-level facts, thereby describing short as well as long-range interactions within an entire document.
The Drag100 dataset is introduced in the paper "GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models"¹. This dataset is a new contribution to the benchmarking of drag editing¹.
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The first benchmark of Python projects that is large-scale, diverse, ready-to-run (i.e., with fully configured and prepared test suites), and ready-to-analyze (i.e., using an integrated Python dynamic analysis framework). The benchmark encompasses 50 popular open-source projects from various application domains, with a total of 681K lines of Python code, and 30K test cases.
This is the replication package for our systematic literature review and can be used for the reproducibility of the individual steps of our search and selection methodology.
Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document which of these skills are being developed in higher education at a similar granularity. Here, we fill this gap by presenting Course-Skill Atlas – a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct Course-Skill Atlas, we apply natural language processing to quantify the alignment between course syllabi and detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these alignment scores to create skill profiles for institutions and academic majors. Our dataset offers a large-scale represent
Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to fill this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new resource to design and evaluate personalized models related to the two tasks of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with both questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and personalization approaches. The results of the preliminary experiments conducted show the appropriatenes
Twitter-HyDrug is a real-world hypergraph data that describes the drug trafficking communities on Twitter. We first crawl the metadata (275,884,694 posts and 40,780,721 users) through the official Twitter API from Dec 2020 to Aug 2021. Afterward, we generate a drug keyword list that covers 21 drug types that may cause drug overdose or drug addiction problems to filter the tweets that contain drug-relevant information. Based on the keyword list, we obtain 266,975 filtered drug-relevant posts by 54,680 users. Moreover, we define six types of drug communities, i.e., cannabis, opioid, hallucinogen, stimulant, depressant, and others communities, based on the drug functions. Six researchers spent 62 days annotating these Twitter users into six communities based on the annotation rules discussed in the next section. With the specific criteria, six researchers annotated the filtered metadata separately. For these Twitter users with disagreed labels, we conducted further discussion among annota
The WRV2 dataset is meticulously assembled to support developing and evaluating video inpainting algorithms aimed specifically at wire removal. This challenging task is critical for enhancing visual aesthetics in various scenes.
This dataset contains LiDAR and IMU data recorded in challenging geometrically degenerate environments. The dataset features 10 sequences recorded in 5 different locations. The data was recorded using an Ouster OS0-128. Ground truth positions were recorded using a Leica MS60 Total Station.
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GCN inference on NeuraChip accelerator on Cora dataset.
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